Overview

Dataset statistics

Number of variables24
Number of observations29965
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory200.0 B

Variable types

Numeric21
Categorical3

Alerts

PAY_SEPT is highly overall correlated with PAY_AUG and 2 other fieldsHigh correlation
PAY_AUG is highly overall correlated with PAY_SEPT and 7 other fieldsHigh correlation
PAY_JUL is highly overall correlated with PAY_SEPT and 9 other fieldsHigh correlation
PAY_JUN is highly overall correlated with PAY_SEPT and 10 other fieldsHigh correlation
PAY_MAY is highly overall correlated with PAY_AUG and 8 other fieldsHigh correlation
PAY_APR is highly overall correlated with PAY_AUG and 8 other fieldsHigh correlation
BILL_AMT_SEPT is highly overall correlated with PAY_AUG and 8 other fieldsHigh correlation
BILL_AMT_AUG is highly overall correlated with PAY_AUG and 10 other fieldsHigh correlation
BILL_AMT_JUL is highly overall correlated with PAY_AUG and 11 other fieldsHigh correlation
BILL_AMT_JUN is highly overall correlated with PAY_JUL and 13 other fieldsHigh correlation
BILL_AMT_MAY is highly overall correlated with PAY_JUL and 13 other fieldsHigh correlation
BILL_AMT_APR is highly overall correlated with PAY_JUN and 11 other fieldsHigh correlation
PAY_AMT_SEPT is highly overall correlated with BILL_AMT_SEPT and 5 other fieldsHigh correlation
PAY_AMT_AUG is highly overall correlated with BILL_AMT_JUL and 5 other fieldsHigh correlation
PAY_AMT_JUL is highly overall correlated with BILL_AMT_JUN and 7 other fieldsHigh correlation
PAY_AMT_JUN is highly overall correlated with BILL_AMT_JUN and 6 other fieldsHigh correlation
PAY_AMT_MAY is highly overall correlated with BILL_AMT_JUN and 5 other fieldsHigh correlation
PAY_AMT_APR is highly overall correlated with BILL_AMT_MAY and 4 other fieldsHigh correlation
PAY_AMT_AUG is highly skewed (γ1 = 30.43861292)Skewed
PAY_SEPT has 14737 (49.2%) zerosZeros
PAY_AUG has 15730 (52.5%) zerosZeros
PAY_JUL has 15764 (52.6%) zerosZeros
PAY_JUN has 16455 (54.9%) zerosZeros
PAY_MAY has 16947 (56.6%) zerosZeros
PAY_APR has 16286 (54.4%) zerosZeros
BILL_AMT_SEPT has 1978 (6.6%) zerosZeros
BILL_AMT_AUG has 2476 (8.3%) zerosZeros
BILL_AMT_JUL has 2840 (9.5%) zerosZeros
BILL_AMT_JUN has 3165 (10.6%) zerosZeros
BILL_AMT_MAY has 3476 (11.6%) zerosZeros
BILL_AMT_APR has 3990 (13.3%) zerosZeros
PAY_AMT_SEPT has 5218 (17.4%) zerosZeros
PAY_AMT_AUG has 5365 (17.9%) zerosZeros
PAY_AMT_JUL has 5937 (19.8%) zerosZeros
PAY_AMT_JUN has 6377 (21.3%) zerosZeros
PAY_AMT_MAY has 6672 (22.3%) zerosZeros
PAY_AMT_APR has 7142 (23.8%) zerosZeros

Reproduction

Analysis started2023-11-04 17:53:25.444625
Analysis finished2023-11-04 17:54:00.754583
Duration35.31 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167442.01
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:00.830583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129760.14
Coefficient of variation (CV)0.77495569
Kurtosis0.53758712
Mean167442.01
Median Absolute Deviation (MAD)90000
Skewness0.99349133
Sum5.0173997 × 109
Variance1.6837693 × 1010
MonotonicityNot monotonic
2023-11-05T00:54:00.936583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3363
 
11.2%
20000 1975
 
6.6%
30000 1610
 
5.4%
80000 1564
 
5.2%
200000 1524
 
5.1%
150000 1107
 
3.7%
100000 1047
 
3.5%
180000 993
 
3.3%
360000 874
 
2.9%
60000 825
 
2.8%
Other values (71) 15083
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1975
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3363
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1564
5.2%
90000 650
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
2
18091 
1
11874 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Length

2023-11-05T00:54:01.030584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T00:54:01.101584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring characters

ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

EDUCATION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8536292
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:01.163584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79041147
Coefficient of variation (CV)0.42641293
Kurtosis2.079207
Mean1.8536292
Median Absolute Deviation (MAD)1
Skewness0.97070927
Sum55544
Variance0.62475029
MonotonicityNot monotonic
2023-11-05T00:54:01.241583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 14019
46.8%
1 10563
35.3%
3 4915
 
16.4%
5 280
 
0.9%
4 123
 
0.4%
6 51
 
0.2%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 10563
35.3%
2 14019
46.8%
3 4915
 
16.4%
4 123
 
0.4%
5 280
 
0.9%
6 51
 
0.2%
ValueCountFrequency (%)
6 51
 
0.2%
5 280
 
0.9%
4 123
 
0.4%
3 4915
 
16.4%
2 14019
46.8%
1 10563
35.3%
0 14
 
< 0.1%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
2
15945 
1
13643 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Length

2023-11-05T00:54:01.340583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T00:54:01.422584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

AGE
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.487969
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:01.531589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2194592
Coefficient of variation (CV)0.25979112
Kurtosis0.043988015
Mean35.487969
Median Absolute Deviation (MAD)6
Skewness0.732056
Sum1063397
Variance84.998429
MonotonicityNot monotonic
2023-11-05T00:54:01.643589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1602
 
5.3%
27 1475
 
4.9%
28 1406
 
4.7%
30 1394
 
4.7%
26 1252
 
4.2%
31 1213
 
4.0%
25 1185
 
4.0%
34 1161
 
3.9%
32 1157
 
3.9%
33 1146
 
3.8%
Other values (46) 16974
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 930
3.1%
24 1126
3.8%
25 1185
4.0%
26 1252
4.2%
27 1475
4.9%
28 1406
4.7%
29 1602
5.3%
30 1394
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

PAY_SEPT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.016752878
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.2%
Negative8432
Negative (%)28.1%
Memory size468.2 KiB
2023-11-05T00:54:01.742102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123492
Coefficient of variation (CV)-67.062627
Kurtosis2.7300384
Mean-0.016752878
Median Absolute Deviation (MAD)1
Skewness0.73460648
Sum-502
Variance1.2622343
MonotonicityNot monotonic
2023-11-05T00:54:01.828115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.2%
-1 5682
 
19.0%
1 3667
 
12.2%
-2 2750
 
9.2%
2 2666
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2750
 
9.2%
-1 5682
 
19.0%
0 14737
49.2%
1 3667
 
12.2%
2 2666
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2666
 
8.9%
1 3667
 
12.2%
0 14737
49.2%
-1 5682
 
19.0%

PAY_AUG
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13185383
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.5%
Negative9798
Negative (%)32.7%
Memory size468.2 KiB
2023-11-05T00:54:01.914419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1963217
Coefficient of variation (CV)-9.0730903
Kurtosis1.5776087
Mean-0.13185383
Median Absolute Deviation (MAD)0
Skewness0.79207041
Sum-3951
Variance1.4311856
MonotonicityNot monotonic
2023-11-05T00:54:02.005420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.5%
-1 6046
 
20.2%
2 3926
 
13.1%
-2 3752
 
12.5%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3752
 
12.5%
-1 6046
 
20.2%
0 15730
52.5%
1 28
 
0.1%
2 3926
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3926
 
13.1%
1 28
 
0.1%
0 15730
52.5%
-1 6046
 
20.2%

PAY_JUL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16439179
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.6%
Negative9989
Negative (%)33.3%
Memory size468.2 KiB
2023-11-05T00:54:02.097014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1958775
Coefficient of variation (CV)-7.2745574
Kurtosis2.091666
Mean-0.16439179
Median Absolute Deviation (MAD)0
Skewness0.84146398
Sum-4926
Variance1.430123
MonotonicityNot monotonic
2023-11-05T00:54:02.214074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.6%
-1 5934
 
19.8%
-2 4055
 
13.5%
2 3819
 
12.7%
3 240
 
0.8%
4 75
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4055
 
13.5%
-1 5934
 
19.8%
0 15764
52.6%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 75
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 75
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.6%
-1 5934
 
19.8%

PAY_JUN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.21892208
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10001
Negative (%)33.4%
Memory size468.2 KiB
2023-11-05T00:54:02.330074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1681752
Coefficient of variation (CV)-5.3360319
Kurtosis3.5089621
Mean-0.21892208
Median Absolute Deviation (MAD)0
Skewness1.0007986
Sum-6560
Variance1.3646333
MonotonicityNot monotonic
2023-11-05T00:54:02.419074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5683
 
19.0%
-2 4318
 
14.4%
2 3159
 
10.5%
3 180
 
0.6%
4 68
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4318
 
14.4%
-1 5683
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 68
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 68
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5683
 
19.0%

PAY_MAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.26450859
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.6%
Negative10051
Negative (%)33.5%
Memory size468.2 KiB
2023-11-05T00:54:02.499074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1322199
Coefficient of variation (CV)-4.2804653
Kurtosis4.0035623
Mean-0.26450859
Median Absolute Deviation (MAD)0
Skewness1.009329
Sum-7926
Variance1.2819218
MonotonicityNot monotonic
2023-11-05T00:54:02.580073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.6%
-1 5535
 
18.5%
-2 4516
 
15.1%
2 2626
 
8.8%
3 178
 
0.6%
4 83
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4516
 
15.1%
-1 5535
 
18.5%
0 16947
56.6%
2 2626
 
8.8%
3 178
 
0.6%
4 83
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 83
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.6%
-1 5535
 
18.5%
-2 4516
 
15.1%

PAY_APR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28943768
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.4%
Negative10601
Negative (%)35.4%
Memory size468.2 KiB
2023-11-05T00:54:02.664074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1490901
Coefficient of variation (CV)-3.9700778
Kurtosis3.4372569
Mean-0.28943768
Median Absolute Deviation (MAD)0
Skewness0.94860899
Sum-8673
Variance1.3204081
MonotonicityNot monotonic
2023-11-05T00:54:02.743436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.4%
-1 5736
 
19.1%
-2 4865
 
16.2%
2 2766
 
9.2%
3 184
 
0.6%
4 48
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4865
 
16.2%
-1 5736
 
19.1%
0 16286
54.4%
2 2766
 
9.2%
3 184
 
0.6%
4 48
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 48
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.4%
-1 5736
 
19.1%
-2 4865
 
16.2%

BILL_AMT_SEPT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22723
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51283.01
Minimum-165580
Maximum964511
Zeros1978
Zeros (%)6.6%
Negative590
Negative (%)2.0%
Memory size468.2 KiB
2023-11-05T00:54:02.840444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13595
median22438
Q367260
95-th percentile201303.8
Maximum964511
Range1130091
Interquartile range (IQR)63665

Descriptive statistics

Standard deviation73658.132
Coefficient of variation (CV)1.4363067
Kurtosis9.7968462
Mean51283.01
Median Absolute Deviation (MAD)21842
Skewness2.6625135
Sum1.5366954 × 109
Variance5.4255205 × 109
MonotonicityNot monotonic
2023-11-05T00:54:02.950637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1978
 
6.6%
390 243
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 48
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27333
91.2%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

BILL_AMT_AUG
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22346
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49236.366
Minimum-69777
Maximum983931
Zeros2476
Zeros (%)8.3%
Negative669
Negative (%)2.2%
Memory size468.2 KiB
2023-11-05T00:54:03.060457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q13010
median21295
Q364109
95-th percentile194889.6
Maximum983931
Range1053708
Interquartile range (IQR)61099

Descriptive statistics

Standard deviation71195.567
Coefficient of variation (CV)1.4459956
Kurtosis10.293212
Mean49236.366
Median Absolute Deviation (MAD)20905
Skewness2.7038617
Sum1.4753677 × 109
Variance5.0688088 × 109
MonotonicityNot monotonic
2023-11-05T00:54:03.168459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2476
 
8.3%
390 230
 
0.8%
326 75
 
0.3%
780 75
 
0.3%
316 72
 
0.2%
2500 51
 
0.2%
396 50
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26837
89.6%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

BILL_AMT_JUL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22026
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47067.916
Minimum-157264
Maximum1664089
Zeros2840
Zeros (%)9.5%
Negative655
Negative (%)2.2%
Memory size468.2 KiB
2023-11-05T00:54:03.270459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12711
median20135
Q360201
95-th percentile187901
Maximum1664089
Range1821353
Interquartile range (IQR)57490

Descriptive statistics

Standard deviation69371.352
Coefficient of variation (CV)1.4738565
Kurtosis19.771003
Mean47067.916
Median Absolute Deviation (MAD)19745
Skewness3.0864938
Sum1.4103901 × 109
Variance4.8123845 × 109
MonotonicityNot monotonic
2023-11-05T00:54:03.376459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2840
 
9.5%
390 274
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 47
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26470
88.3%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

BILL_AMT_JUN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21548
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43313.33
Minimum-170000
Maximum891586
Zeros3165
Zeros (%)10.6%
Negative675
Negative (%)2.3%
Memory size468.2 KiB
2023-11-05T00:54:03.483458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12360
median19081
Q354601
95-th percentile174469.8
Maximum891586
Range1061586
Interquartile range (IQR)52241

Descriptive statistics

Standard deviation64353.514
Coefficient of variation (CV)1.485767
Kurtosis11.298582
Mean43313.33
Median Absolute Deviation (MAD)18681
Skewness2.8205448
Sum1.2978839 × 109
Variance4.1413748 × 109
MonotonicityNot monotonic
2023-11-05T00:54:04.050071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3165
 
10.6%
390 245
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 43
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
416 33
 
0.1%
Other values (21538) 26136
87.2%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

BILL_AMT_MAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21010
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40358.334
Minimum-81334
Maximum927171
Zeros3476
Zeros (%)11.6%
Negative655
Negative (%)2.2%
Memory size468.2 KiB
2023-11-05T00:54:04.152114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11787
median18130
Q350247
95-th percentile165805.6
Maximum927171
Range1008505
Interquartile range (IQR)48460

Descriptive statistics

Standard deviation60817.131
Coefficient of variation (CV)1.5069287
Kurtosis12.294539
Mean40358.334
Median Absolute Deviation (MAD)17714
Skewness2.874925
Sum1.2093375 × 109
Variance3.6987234 × 109
MonotonicityNot monotonic
2023-11-05T00:54:04.250227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3476
 
11.6%
390 234
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 46
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25804
86.1%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

BILL_AMT_APR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20604
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38917.012
Minimum-339603
Maximum961664
Zeros3990
Zeros (%)13.3%
Negative688
Negative (%)2.3%
Memory size468.2 KiB
2023-11-05T00:54:04.345265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11262
median17124
Q349252
95-th percentile161932
Maximum961664
Range1301267
Interquartile range (IQR)47990

Descriptive statistics

Standard deviation59574.148
Coefficient of variation (CV)1.5307996
Kurtosis12.259126
Mean38917.012
Median Absolute Deviation (MAD)16808
Skewness2.8451372
Sum1.1661483 × 109
Variance3.5490791 × 109
MonotonicityNot monotonic
2023-11-05T00:54:04.456347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3990
 
13.3%
390 206
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 44
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25327
84.5%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

PAY_AMT_SEPT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5670.0993
Minimum0
Maximum873552
Zeros5218
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:04.560400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2102
Q35008
95-th percentile18447.2
Maximum873552
Range873552
Interquartile range (IQR)4008

Descriptive statistics

Standard deviation16571.849
Coefficient of variation (CV)2.9226736
Kurtosis414.85486
Mean5670.0993
Median Absolute Deviation (MAD)1929
Skewness14.661595
Sum1.6990453 × 108
Variance2.7462619 × 108
MonotonicityNot monotonic
2023-11-05T00:54:04.658566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5218
 
17.4%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19504
65.1%
ValueCountFrequency (%)
0 5218
17.4%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

PAY_AMT_AUG
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7899
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5927.9832
Minimum0
Maximum1684259
Zeros5365
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:04.755707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1850
median2010
Q35000
95-th percentile19030.8
Maximum1684259
Range1684259
Interquartile range (IQR)4150

Descriptive statistics

Standard deviation23053.457
Coefficient of variation (CV)3.8889207
Kurtosis1639.9245
Mean5927.9832
Median Absolute Deviation (MAD)1990
Skewness30.438613
Sum1.7763202 × 108
Variance5.3146186 × 108
MonotonicityNot monotonic
2023-11-05T00:54:04.862706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5365
 
17.9%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19359
64.6%
ValueCountFrequency (%)
0 5365
17.9%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

PAY_AMT_JUL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5231.6888
Minimum0
Maximum896040
Zeros5937
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:04.968750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1804
Q34512
95-th percentile17602.6
Maximum896040
Range896040
Interquartile range (IQR)4122

Descriptive statistics

Standard deviation17616.361
Coefficient of variation (CV)3.3672418
Kurtosis563.73928
Mean5231.6888
Median Absolute Deviation (MAD)1796
Skewness17.208177
Sum1.5676756 × 108
Variance3.1033618 × 108
MonotonicityNot monotonic
2023-11-05T00:54:05.068750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5937
 
19.8%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18382
61.3%
ValueCountFrequency (%)
0 5937
19.8%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

PAY_AMT_JUN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6937
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4831.6175
Minimum0
Maximum621000
Zeros6377
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:05.164750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1500
Q34016
95-th percentile16037
Maximum621000
Range621000
Interquartile range (IQR)3716

Descriptive statistics

Standard deviation15674.465
Coefficient of variation (CV)3.2441444
Kurtosis277.04869
Mean4831.6175
Median Absolute Deviation (MAD)1500
Skewness12.898506
Sum1.4477942 × 108
Variance2.4568884 × 108
MonotonicityNot monotonic
2023-11-05T00:54:05.265749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6377
 
21.3%
1000 1394
 
4.7%
2000 1214
 
4.1%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17582
58.7%
ValueCountFrequency (%)
0 6377
21.3%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

PAY_AMT_MAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4804.897
Minimum0
Maximum426529
Zeros6672
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:05.368749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1261
median1500
Q34042
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3781

Descriptive statistics

Standard deviation15286.372
Coefficient of variation (CV)3.1814152
Kurtosis179.87521
Mean4804.897
Median Absolute Deviation (MAD)1500
Skewness11.121742
Sum1.4397874 × 108
Variance2.3367318 × 108
MonotonicityNot monotonic
2023-11-05T00:54:05.467749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6672
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17202
57.4%
ValueCountFrequency (%)
0 6672
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

PAY_AMT_APR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6939
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5221.498
Minimum0
Maximum528666
Zeros7142
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-11-05T00:54:05.564749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1131
median1500
Q34000
95-th percentile17384.4
Maximum528666
Range528666
Interquartile range (IQR)3869

Descriptive statistics

Standard deviation17786.977
Coefficient of variation (CV)3.4064893
Kurtosis166.98179
Mean5221.498
Median Absolute Deviation (MAD)1500
Skewness10.635094
Sum1.5646219 × 108
Variance3.1637655 × 108
MonotonicityNot monotonic
2023-11-05T00:54:05.666749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7142
23.8%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.1%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16834
56.2%
ValueCountFrequency (%)
0 7142
23.8%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
0
23335 
1
6630 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Length

2023-11-05T00:54:05.759749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T00:54:05.828748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Interactions

2023-11-05T00:53:58.826884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.037273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.568469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.092060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.649107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.130402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.606817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.101187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.574028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.067612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.521652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.080950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.653767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.221282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.729717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.220144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.835358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.648226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.287042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.781373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.320071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.900883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.121273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.643469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.164060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.722107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.203402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.676862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.170186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.646178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.137612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.594653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.154331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.728823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.294282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.798716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.296146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.906317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.736227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.358041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.854372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.390070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.974807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.195360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.715470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.237060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.799107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.272401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.747913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.240185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.719178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.207680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.668652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.230189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.804864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.366282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.869808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.372191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.976349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.816386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.430041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.931373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.460070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:59.048808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.268406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.789469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.309061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.869172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.343402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.823645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.312186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.797179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.277726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.742692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.307866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.881863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.438282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.940866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.447698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:51.048207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.903391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.500041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:56.014890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.531118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:59.119918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.337405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.859468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.381059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.937214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.411402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.890645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.380185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.868178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.344773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.813753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.424499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.954921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.507343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:48.007901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.521754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:51.116292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.977965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.571098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:56.085890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.600117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:59.188918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.411405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.928555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.451060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:32.007338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.478401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.958647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.448185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.946336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.410817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.885792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.504498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:45.028921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.577343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:48.076962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.596754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:51.187329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:53.051018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.640151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:56.156889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.672118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:59.260918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:27.483405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.998605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.520104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:32.077847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.546401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:35.024645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.516186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.015386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:39.478817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.955845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.576498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:45.101921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.646343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:48.145015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-11-05T00:53:34.251660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:35.745187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.223028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.715430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.174509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:42.710849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.293639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:45.848204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.363645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:48.862403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.455336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.298228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:53.901836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.425040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:56.959982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.464635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:54:00.064641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.287467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:29.812099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.356383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:32.854295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.326660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:35.822187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.298028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.791430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.249562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:42.789849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.373639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:45.929204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.439644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:48.943021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.538363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.373227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:53.981838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.501041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.037982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.543747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:54:00.134965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.357469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:29.881060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.428679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:32.920360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.397660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:35.888186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.365028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.859535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.315606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:42.860849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.441680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:45.999204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.506679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.011414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.614317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.439227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.055968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.567040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.107025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.612781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:54:00.208444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.430469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:29.955060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.504200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:32.993402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.471663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:35.963187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.437028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.931498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.386606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:42.936949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.514680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.077246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.579679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.083997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.691825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.511227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.137042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.642373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.180025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.690379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:54:00.275441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:28.497469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:30.022060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:31.576598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:33.060402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:34.538817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:36.032186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:37.504028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:38.997547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:40.453653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:43.006949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:44.582680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:46.147246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:47.654717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:49.151105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:50.761886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:52.576227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:54.212042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:55.711373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:57.249025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T00:53:58.758378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-05T00:54:05.906807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIMIT_BALEDUCATIONAGEPAY_SEPTPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBILL_AMT_SEPTBILL_AMT_AUGBILL_AMT_JULBILL_AMT_JUNBILL_AMT_MAYBILL_AMT_APRPAY_AMT_SEPTPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRSEXMARRIAGEdefault payment next month
LIMIT_BAL1.000-0.2630.186-0.297-0.342-0.331-0.308-0.285-0.2640.0550.0500.0620.0740.0820.0890.2740.2800.2860.2850.2950.3190.0740.0640.157
EDUCATION-0.2631.0000.1590.1330.1680.1610.1510.1360.1230.0940.0920.0800.0680.0590.055-0.043-0.048-0.043-0.044-0.051-0.0540.0290.1140.072
AGE0.1860.1591.000-0.064-0.084-0.084-0.081-0.083-0.0760.0010.0010.002-0.004-0.001-0.0000.0330.0440.0330.0400.0380.0390.0910.2860.048
PAY_SEPT-0.2970.133-0.0641.0000.6290.5500.5180.4880.4650.3160.3310.3160.3080.3000.290-0.098-0.063-0.054-0.034-0.026-0.0450.0590.0360.422
PAY_AUG-0.3420.168-0.0840.6291.0000.7990.7120.6730.6340.5700.5500.5170.4960.4770.4580.0180.0810.0850.0930.0970.0800.0710.0340.340
PAY_JUL-0.3310.161-0.0840.5500.7991.0000.8000.7180.6700.5230.5870.5560.5300.5060.4830.2140.0340.1010.1170.1220.0960.0670.0320.294
PAY_JUN-0.3080.151-0.0810.5180.7120.8001.0000.8220.7310.5110.5570.6180.5920.5600.5320.1830.2440.0670.1420.1600.1410.0630.0350.278
PAY_MAY-0.2850.136-0.0830.4880.6730.7180.8221.0000.8200.4980.5360.5860.6490.6170.5780.1730.2200.2580.1040.1830.1700.0560.0330.269
PAY_APR-0.2640.123-0.0760.4650.6340.6700.7310.8201.0000.4870.5220.5600.6050.6670.6290.1760.1980.2360.2820.1390.1960.0470.0300.249
BILL_AMT_SEPT0.0550.0940.0010.3160.5700.5230.5110.4980.4871.0000.9110.8570.8070.7690.7340.5010.4710.4390.4410.4240.4090.0260.0170.031
BILL_AMT_AUG0.0500.0920.0010.3310.5500.5870.5570.5360.5220.9111.0000.9080.8480.8030.7650.6350.4960.4670.4600.4480.4280.0330.0120.031
BILL_AMT_JUL0.0620.0800.0020.3160.5170.5560.6180.5860.5600.8570.9081.0000.9030.8480.8040.5490.6370.4910.4870.4760.4570.0180.0120.000
BILL_AMT_JUN0.0740.068-0.0040.3080.4960.5300.5920.6490.6050.8070.8480.9031.0000.9030.8480.5110.5540.6330.5060.5030.4800.0260.0140.019
BILL_AMT_MAY0.0820.059-0.0010.3000.4770.5060.5600.6170.6670.7690.8030.8480.9031.0000.9020.4810.5140.5480.6460.5240.5080.0210.0150.017
BILL_AMT_APR0.0890.055-0.0000.2900.4580.4830.5320.5780.6290.7340.7650.8040.8480.9021.0000.4550.4860.5180.5690.6660.5280.0260.0160.022
PAY_AMT_SEPT0.274-0.0430.033-0.0980.0180.2140.1830.1730.1760.5010.6350.5490.5110.4810.4551.0000.5110.5180.4850.4670.4540.0000.0310.027
PAY_AMT_AUG0.280-0.0480.044-0.0630.0810.0340.2440.2200.1980.4710.4960.6370.5540.5140.4860.5111.0000.5150.5190.4960.4900.0000.0200.013
PAY_AMT_JUL0.286-0.0430.033-0.0540.0850.1010.0670.2580.2360.4390.4670.4910.6330.5480.5180.5180.5151.0000.5150.5330.5050.0120.0190.024
PAY_AMT_JUN0.285-0.0440.040-0.0340.0930.1170.1420.1040.2820.4410.4600.4870.5060.6460.5690.4850.5190.5151.0000.5330.5460.0000.0300.022
PAY_AMT_MAY0.295-0.0510.038-0.0260.0970.1220.1600.1830.1390.4240.4480.4760.5030.5240.6660.4670.4960.5330.5331.0000.5480.0140.0000.035
PAY_AMT_APR0.319-0.0540.039-0.0450.0800.0960.1410.1700.1960.4090.4280.4570.4800.5080.5280.4540.4900.5050.5460.5481.0000.0130.0000.028
SEX0.0740.0290.0910.0590.0710.0670.0630.0560.0470.0260.0330.0180.0260.0210.0260.0000.0000.0120.0000.0140.0131.0000.0320.039
MARRIAGE0.0640.1140.2860.0360.0340.0320.0350.0330.0300.0170.0120.0120.0140.0150.0160.0310.0200.0190.0300.0000.0000.0321.0000.033
default payment next month0.1570.0720.0480.4220.3400.2940.2780.2690.2490.0310.0310.0000.0190.0170.0220.0270.0130.0240.0220.0350.0280.0390.0331.000

Missing values

2023-11-05T00:54:00.394947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-05T00:54:00.623568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_SEPTPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBILL_AMT_SEPTBILL_AMT_AUGBILL_AMT_JULBILL_AMT_JUNBILL_AMT_MAYBILL_AMT_APRPAY_AMT_SEPTPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRdefault payment next month
0200002212422-1-1-2-239133102689000068900001
112000022226-1200022682172526823272345532610100010001000020001
290000222340000002923914027135591433114948155491518150010001000100050000
350000221370000004699048233492912831428959295472000201912001100106910000
45000012157-10-100086175670358352094019146191312000366811000090006896790
5500001123700000064400570695760819394196192002425001815657100010008000
6500000112290000003679654120234450075426534830034739445500040000380002023913750137700
7100000222230-1-100-111876380601221-1595673806010581168715420
81400002312800200011285140961210812211117933719332904321000100010000
92000013235-2-2-2-2-1-10000130071391200013007112200
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_SEPTPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBILL_AMT_SEPTBILL_AMT_AUGBILL_AMT_JULBILL_AMT_JUNBILL_AMT_MAYBILL_AMT_APRPAY_AMT_SEPTPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRdefault payment next month
299901400001214100000013832513714213911013826249675461216000700042281505200020000
29991210000121343222222500250025002500250025000000001
299921000013143000-2-2-288021040000002000000000
29993100000112380-1-100030421427102996706266947355004200011178440003000200020000
2999480000122342222227255777708793847751982607811587000350007000040001
299952200001313900000018894819281520836588004312371598085002000050033047500010000
2999615000013243-1-1-1-100168318283502897951900183735268998129000
299973000012237432-10035653356275820878205821935700220004200200031001
2999880000131411-1000-1-16457837976304527741185548944859003409117819265296418041
2999950000121460000004792948905497643653532428153132078180014301000100010001